With the clean sequences of 4,000 IDs, we generate the 3D SMPL parameter, 3D mesh, 3D pose, 2D silhouette, and 2D pose for each frame.
For the 3D SMPL, 3D mesh, and 3D pose, we exploit a state-of-the-art 3D human mesh recovery method, ROMP https://github.com/Arthur151/ROMP under the MIT License. [1]}, since it can efficiently output these three representations in an end-to-end framework.
For the 2D silhouette, we use the semantic segmentation method, HRNet-segmentation https://github.com/HRNet/HRNet-Semantic-Segmentation under the MIT license. [2]}, to obtain the silhouette of the person in each frame.
For the 2D pose, we also utilize the HRNet to estimate the 2D keypoints of the person in each frame.
We keep the original resolution and aspect ratio of the frame without resizing or normalization.
Some examples of gait representations in our dataset are shown in Figure REF .
It is worth noting that we will only release the generated gait representations but not release any RGB frames to protect the privacy of the subjects.
